Systems and methods for detecting early indications of illnesses

ABSTRACT

A method for addressing tactical situations via tactical devices may include (i) identifying at least one wearable device that monitors at least one biomarker of a user wearing the at least one wearable device during a span of time while carrying out daily activities, (ii) receiving, by a server, information about activity of the at least one biomarker monitored by the at least one wearable device during the span of time, (iii) determining that the activity of the at least one biomarker during the span of time includes an early indication of an illness, and (iv) transmitting an alert about the early indication of the illness detected by the at least one wearable device. Various other systems, and methods are also disclosed.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of claims the benefit of U.S.application Ser. No. 16/884,043 filed 27 May 2020, the disclosure ofwhich is incorporated, in its entirety, by this reference.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings illustrate a number of exemplary embodimentsand are a part of the specification. Together with the followingdescription, these drawings demonstrate and explain various principlesof the instant disclosure.

FIG. 1 is an illustration of example wearable devices and an examplemobile device.

FIG. 2 is an illustration of an example user wearing wearable deviceswhile performing daily activities.

FIG. 3 is an illustration of an example user interacting with a mobiledevice.

FIG. 4 is diagram of an example system for detecting early indicationsof illnesses.

FIG. 5 is diagram of an example system for detecting early indicationsof illnesses and collecting other metrics about a team.

FIG. 6 is a block diagram of an example system for detecting earlyindications of illnesses.

FIG. 7 is a flow diagram of an example method for detecting earlyindications of illnesses.

Throughout the drawings, identical reference characters and descriptionsindicate similar, but not necessarily identical, elements. While theexemplary embodiments described herein are susceptible to variousmodifications and alternative forms, specific embodiments have beenshown by way of example in the drawings and will be described in detailherein. However, the exemplary embodiments described herein are notintended to be limited to the particular forms disclosed. Rather, theinstant disclosure covers all modifications, equivalents, andalternatives falling within the scope of the appended claims.

DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS

The present disclosure is generally directed to systems and methods fordetecting early indications of illness via biomarker data from wearabledevices. As will be explained in greater detail below, by collectingbiomarker data from wearable devices and then analyzing the data forearly indications of illness, the systems described herein may identifythe onset of an illness before obvious symptoms (e.g., coughing,sneezing, high fever, etc.) become apparent. Identifying an illnessearly may help the user seek treatment earlier as well as prevent theuser from infecting additional people, improving health outcomes forentire communities. By automatically collecting biomarker data viawearable devices that the user wears while performing everydayactivities, the systems described herein may track biomarkers withoutinterfering with a user's life and without gaps in data caused by usersforgetting to collect data manually (e.g., via a thermometer or othernon-wearable device). Additionally, the systems and methods describedherein may improve the functioning of a computing device that analyzesbiomarker data by providing the computing device with consistent,high-quality biomarker data. As discussed in greater detail below, thevarious embodiments disclosed herein, whether used alone or incombination, may improve the field of healthcare by identifying earlyindications of illnesses, leading to more proactive treatment anddecreased community transmission.

In some embodiments, the systems described herein may include a mobileapplication that collects data from wearable devices. FIG. 1 is anillustration of an example set of wearable devices that send data to amobile device. The term “wearable device,” as used herein, may generallyrefer to any wireless device designed to be worn during everydayactivity (as opposed to, e.g., medical devices designed to be worn whileconducting tests) that is capable of detecting, storing, and/ortransmitting data about one or more biomarkers. In some embodiments, awearable device may include a general-purpose smart device, such as asmartwatch, that has functions unrelated to biomarker data. Additionallyor alternatively, a wearable device may include a purpose-built wearabledevice that primarily has functions related to biomarker data, such as achest strap for monitoring breathing and/or heart activity duringexercise. In some examples, a wearable device may be an exercisemonitor, sleep monitor, and/or other type of non-prescribed and/orcommercial off-the-shelf monitor. In some embodiments, a wearable devicemay be produced by a third-party vendor that does not produce thesystems described herein. Examples of form factors for wearable devicesmay include, without limitation, watches, bracelets, armbands, rings,chest straps, thigh straps, anklets, forehead straps, hats, necklaces,belts, shirts, pants, shoes, and/or gloves. Examples of wearable devicesmay include, without limitation, OURA rings, POLAR VANTAGE watches,POLAR chest straps, APPLE watches, GARMIN heart rate monitors, and/orBODIMETRICS CIRCUL rings. In some embodiments, the systems describedherein may use data from wearable devices produced by multiple differentthird-party vendors. For example, the systems described herein mayretrieve breathing data and/or heart rate data from a POLAR chest strap,sleep data and/or heart rate data from an OURA ring, and heart rate datafrom an APPLE watch. In another example, the systems described hereinmay retrieve data from a smart watch and a ring, from a chest strap anda smart watch, or from a chest strap and a ring.

In one embodiment, a wearable device 102 may include a chest strap thatmonitors biomarkers such as heart rate. Additionally or alternatively, awearable device 104 may include a ring that monitors biomarkers such assleep quality, heart rate, and/or temperature. In one example, wearabledevice 102 and/or wearable device 104 may send data to a mobile device106 that is configured with a biomarker monitoring application. In someembodiments, wearable device 102 and wearable device 104 may both senddata directly to the biomarker monitoring application. Additionally oralternatively, wearable device 102 and/or wearable device 104 may eachsend data to device-specific applications and the biomarker monitoringapplication may collect the data from the device-specific applications.The term “biomarker,” as used herein, generally refers to anymeasurable, variable aspect of a person's physiology. In some examples,a biomarker and/or a change in a biomarker may be indicative of anillness. Examples of biomarkers may include, without limitation, heartrate, heart rate variability, temperature, sleep duration, sleepdisruption, levels of various chemicals, proteins, and/or cells (e.g.,white blood cells, hormones, etc.), and/or breathing patterns.

In some examples, a user may wear one or more wearable devices whileperforming everyday activities. The term “everyday activities” or “dailyactivities,” as used herein, generally refers to activities that arepart of a user's typical day, professional duties, and/or routine, asopposed to activities specifically organized to collect data. Forexample, going for a jog outside or on a treadmill at the gym may be adaily activity for a user while going for a jog on a hospital treadmillwhile monitored by medical devices may not be a daily activity. Inanother example, performing physical training may be a daily activityfor a member of the military. Similarly, lifting a heavy patient onto astretcher may be a daily activity for a paramedic. Sleeping, eating,walking, exercising, and/or performing professional duties may all beexamples of daily activities. In some embodiments, a user may wearwearable devices continuously for one or more days. In one example, auser may wear every wearable device in a set of wearable devices (e.g.,a watch, chest strap, and/or ring) continuously. In another example, auser may wear a set of wearable devices continuously by wearing somewearable devices during some spans of time (e.g., a watch while awake)and other wearable devices during other spans of time (e.g., a sleeptracking ring during sleep) such that continuous data collection by thewearable devices as a set is possible.

In one example, as illustrated in FIG. 2, a user 206 may wear a wearabledevice 202 and/or a wearable device 204 while exercising. In someexamples, wearable device 202 may be a chest strap that monitors heartrate and/or wearable device 204 may be a ring that monitors temperature,heart rate, blood oxygen saturation, and/or other biomarkers. In someembodiments, a wearable device such as a ring or watch may capturephotoplethysmographic (PPG) signals and/or may use PPG signals todetermine heart rate and/or heart rate variability. In one embodiment,the systems described herein may use 3-axis accelerometry (e.g., asrecorded by the accelerometer of a wearable device such as wearabledevice 202 and/or 204) to determine movement. In some examples, thesystems described herein may track daytime and/or nighttime bodytemperature via a negative temperature coefficient sensor in a wearabledevice such as a ring and/or watch. In one embodiment, the systemsdescribed herein may monitor biomarker activity over time. For example,the systems described herein may establish a baseline of the user'sbaroreflex and/or other pressure-related mechanism of the heart with awearable device such as wearable device 202. In one example, the systemsdescribed herein may compare the heart rate of user 206 (e.g., asrecorded by wearable device 202) while exercising to previously recordedheart rates to determine if the current heart rate is higher than istypical for this type of activity (e.g., aerobic exercise). In oneexample, the systems described herein may monitor the heart rate of user206 after exercise to measure when the heart rate returns to resting.

In some examples, changes in heart rate compared to normal duringexercise and/or during recovery and/or changes in recovery time may bean early indication of an illness. The term “early indication,” as usedherein, generally refers to changes in biomarkers that take place beforethe onset of the more readily identifiable symptoms of an illness. Forexample, a change in heart rate variability may be an early indicationwhile a high fever may be a readily identifiable symptom. In anotherexample, sleep disruption may be an early indication while a persistentcough may be a readily identifiable symptom. In some examples, an earlyindication of an illness may be a combination of changes to biomarkers.For example, coronavirus disease 2019 (COVID-19) infections have adistinctive pattern in the inflammatory response caused in the patient'soverall system, lungs, and heart. In one example, the systems describedherein may detect the changes that this inflammatory response causes inheart rate and/or heart rate variability as early indications of aCOVID-19 infection.

In some embodiments, the systems described herein may surface biomarkerand/or illness indication data to a user via a mobile device. Forexample, as illustrated in FIG. 3, a user 310 may wear multiple wearabledevices, such as wearable devices 302, 304, and/or 306, that may sendbiomarker data to a mobile device 308. In some embodiments, user 310 mayview biomarker data and/or any detected indications of illness via anapp on mobile device 308. Although illustrated as a mobile phone, mobiledevice 308 may represent any suitable mobile device. Additionally oralternatively, user 310 may view data collected and/or produced by thesystems described herein on a smart wearable device, such as wearabledevice 306. In some examples, user 310 may view data collected and/orproduced by the systems described herein on a computing device, such asa laptop or desktop computer. In some embodiments, the systems describedherein may enable user 310 to configure settings for monitoring and/ordevices. For example, mobile device 308 may receive input from user 310specifying a specific illness for which to monitor for earlyindications. Additionally or alternatively, mobile device 308 mayreceive input from user 310 specifying additional devices to which tosend alerts if early indications of an illness are detected. In someembodiments, the systems described herein may send an alert thatincludes a recommended action or actions for the user based on theprobability that the user is ill and/or the type of illness. Forexample, if the systems described herein determine that the user has agreater than 50% chance of being infected with COVID-19, the systemsdescribed herein may send an alert that prompts the user to self-isolatefor two weeks. In some embodiments, mobile device 308 may prompt user310 to manually enter health information and/or other information. Forexample, mobile device 308 may prompt user 310 to fill out a dailyhealth survey. In some embodiments, mobile device 308 may prompt a userto fill out a survey in response to detecting potential earlyindications of an illness.

In some embodiments, the systems described herein may send data to oneor more servers for processing. For example, as illustrated in FIG. 4, amobile device 402 may receive biomarker data from wearable devices 406and/or 408 and may send that data to a server 410 for processing and/oranalysis. In one embodiment, server 410 may clean, format, and/orotherwise pre-process the data before sending the data to a server 412that analyzes the processed biomarker data for early indications ofillness. In one example, server 412 may be a specialized medical serveroperated by a third party, such as a medical institution. In otherembodiments, server 410 may both process and analyze the data. In someembodiments, the systems described herein may rate different types ofdata from different types of wearable devices at a different level ofreliability. For example, the systems described herein may rate sleepdisruption data from a chest strap such as wearable device 408 lowerthan sleep disruption data from a ring such as wearable device 406. Inanother example, the systems described herein may rate heart rate datafrom a chest strap and a ring as equally reliable. In one embodiment,server 410 may process the biomarker data by discarding and/or assigninga lower weighting to data collected by wearable devices with a lowerreliability rating.

In some embodiments, a computing device 404 may receive data from mobiledevice 402 and/or server 410. Although illustrated as a laptop,computing device 404 may generally represent any type of personalcomputing device and/or mobile device. In some examples, computingdevice 404 may be operated by a team leader of a team that includes theoperator of mobile device 402. For example, a squad leader of a militarysquad, a team leader of a team of paramedics, and/or any other suitabletype of team leader. In one embodiment, the systems described herein maytransmit an alert to computing device 404 in response to detecting anearly indication of an illness in any team member. In some embodiments,the systems described herein may receive input from computing device 404specifying what illness or type of illness (e.g., the cold, the flu,COVID-19, etc.) to detect early indications of. In some examples,computing device 404 may display biomarkers and/or other relevant datacollected by wearable devices of team members to enable the team leaderto make decisions about the team members (e.g., whether to send a teammember to work or to get medical attention). In one embodiment, thesystems described herein may correlate team members with team leaders bydisplaying a key (e.g., a numeric key) on team member devices that, whenentered into a team leader device, registers the team member device aspart of the team. In some embodiments, a team leader device may be adevice that is configured with a setting identifying the device as ateam leader device and/or a device that is associated with a team leaderaccount (e.g., an account that has permission to view data for otheruser accounts). In some embodiments, a team leader device and/or a teamleader account may have special settings, permissions, and/or hardwareand/or software configurations that are not present in normal userdevices and/or accounts.

In some embodiments, a team leader may receive data from multiple setsof wearables worn by multiple members of a team. For example, asillustrated in FIG. 5, a set of wearable devices 502 worn by a firstteam member may send data to a mobile device 504 that then sends thatdata to a server 506. Similarly, a set of wearable devices 512 worn by asecond team member may send data to a mobile device 514 that then sendsthat data to server 506 and/or a set of wearable devices 522 worn by athird team member may send data to a mobile device 524 that then sendsthat data to server 506. In one embodiment, server 506 may process thebiomarker data for indications of illness and/or other information andmay send the processed data and/or biomarker data to a computing device508 operated by a team leader. Although illustrated as a single team ofthree members, in some embodiments, multiple teams of any number ofmembers may send data to a server or servers to inform decision-makingby a team leader.

In some embodiments, the systems described herein may analyze biomarkerdata for additional information beyond early indications of illness. Forexample, the systems described herein may analyze biomarker data formetrics that predict a team member's ability and/or readiness tocomplete a task. In one example, the systems described herein mayanalyze physiological data about a team member to determine a teammember's ability to run, scale physical obstacles, and/or perform otherphysical tasks necessary to achieve a potential team goal. In someembodiments, wearables may collect data in addition to biomarkers, suchas location (e.g., global positioning system coordinates) that mayenable the collection of metrics such as the time it takes a team memberto traverse a certain distance. In some examples, a team leader may usebiomarker data and/or derived metrics to make determinations aboutwhether to assign a team to accomplish a certain goal, assign a team torest, assign a team to train, transfer team members between teams,and/or other relevant determinations.

In some examples, a team leader and/or other high-level decision-makermay use data provided by the systems described herein to allocateresources. In some embodiments, the systems described herein may prompta team leader with resource allocation suggestions. For example, if oneor more teams show early indications of an illness, the systemsdescribed herein may prompt a team leader to allocate additionalresources (e.g., personal protective equipment, testing equipment,medical equipment, medical personnel, medicine, etc.) to the divisionand/or geographical area with the team in preparation for a potentialoutbreak of illness.

In some embodiments, the systems described herein may include varioussoftware applications and/or modules. For example, as illustrated inFIG. 6, a wearable device 604 may send data to a device-specific app 608and/or a wearable device 606 may send data to a device-specific app 610.In one embodiment, an app 612 may retrieve data from app 608 and/or app610. In some embodiments, app 612 may retrieve data via an applicationprogramming interface (API). Additionally or alternatively, app 612 mayintercept data sent to and/or received by app 608 and/or app 610. In oneembodiment, app 612 may query a database to which app 608 and/or app 610sends data rather than retrieving data directly from app 608 and/or app610.

In some embodiments, app 612 may send data to a data processing module614 hosted on a server 620. In one embodiment, data processing module614 may remove any personally identifying information from the data.Additionally or alternatively, the systems described herein may removepersonally identifying information before sending the data to server 620(e.g., via app 612). In some embodiments, data processing module 614 mayclean, format, and/or otherwise process data before sending theprocessed data to a data analysis module 616. In some examples, dataprocessing module 614 may aggregate, regularize, and/or normalize datarecorded by multiple different wearable devices that are each producedby a different third-party vendor and thus each format data in differentways.

In some embodiments, the systems described herein may compare (e.g.,during an initial training phase) data gathered by commercialoff-the-shelf wearable devices to clinical gold standards for data inorder to account for variations in quality of data gathered byoff-the-shelf wearable devices. In some embodiments, the fidelity of thediagnostic prediction and/or result may be dependent upon how much thedata from the commercial sensors deviate from clinical gold standards.In some examples, if a suite of commercial wearable sensors are includedas data source but clinical sensors are not available to be used toproduce training data, the systems described herein may use thecommercial sensors that are closest to the clinical gold standard toestablish the algorithm baseline. In some embodiments, the systemsdescribed herein may derive diagnostic predictions and/or results fromthe rest of the sensors in the suite (e.g., the sensors farther from theclinical gold standard) with adjusted confidence values. For example,the systems described herein may rate wearable devices that gather lowerquality data with lower confidence.

In one embodiment, data processing module 614 may perform outlierrejection, amplitude normalization, and/or bandpass filtering. In someembodiments, the systems described herein may use accelerometers as inindication for signal quality. In some examples, after the preprocessingstep, the systems described herein may analyze metrics correlated tohealth conditions, such as the standard deviation of the normal to thenormal interval for heart rate variability. In some embodiments, thesystems described herein may combine heart rate data with accelerometerdata to classify a user's activity level. In one embodiment, the systemsdescribed herein may aggregate and/or normalize data across time so thatthe combination of biomarkers from the various wearable devices areanalyzed according to synchronized timelines.

In one embodiment, data analysis module 616 may analyze biomarker datacollected by various wearable devices to detect early indications ofillness. Although illustrated as a single server, server 620 mayrepresent multiple physical and/or virtual servers in the same ordifferent physical locations (e.g., cloud servers). Data analysis module616 may perform a variety of types of analysis. For example, dataanalysis module 616 may apply one or more machine-learning techniquesand/or classifiers, such as utilizing convolutional neural networks toperform the predictive analysis of biomarker data. In some examples, thesystems described herein may generate a neural network with data setsthat include wearable-based physiological data (e.g., heart ratevariation features, temperature, etc.), activity features (e.g., sleeppatterns, leisure, exercise, rigorous exercise, exercise recovery,etc.), socio-demographics (e.g., age, race, gender, etc.), medicalhistory (e.g., pre-existing conditions such as hypertension, currentmedications, etc.), and/or physical conditions (e.g., body mass index,resting heart rate, blood pressure, oxygen saturation, etc.). In oneembodiment, the data quality of the medical history and/or physicalcondition data may be marked manually and the systems described hereinmay weight the data according to the data quality markers.

In some embodiments, the systems described herein may train one or moremachine learning models (e.g., neural networks) using generalstatistical methods. In one embodiment, the systems described herein mayevaluate, at various sensitivity levels, the variables and thecombinations of variables within the models for the specificity,accuracy, and positive predictive value for symptoms and diagnosis of agiven illness (e.g., the seasonal flu, COVID-19, etc.). In someembodiments, the systems described herein may check this set ofvariables against the user's baseline physiological model as the systemsdescribed herein ingest new datasets in order to scan for deviationsfrom norm. In one embodiment, the systems described herein may form abeta model with variables with the highest positive predictive values ina preset period of time before symptoms are observed and documented. Insome examples, the beta models may then be formed into networks thatrepresent the manifestation of the infection in each of the users. Inone example, the systems described herein may then train the networkedgraphs of the models with collected data from the wearable devices togenerate predictive diagnoses for the general population.

In some examples, the systems described herein may identify and/or trackoutbreaks of novel illnesses. For example, if the symptoms describedherein detect a set of similar early indications of illness acrossmultiple users and cannot match the set of early indications to a knownillness, the systems described herein may transmit an alert (e.g., to ateam leader or other high-level decision-maker with access to data frommultiple users) that a potential novel illness has been detected. Bytracking clusters of early indications in this way, the systemsdescribed herein may enable quick detection of novel illnesses and/orbioterror attacks. In some embodiments, the systems described herein mayalert one or more predetermined organizations (e.g., a government agencytasked with public health) in response to detecting a possible outbreakof a novel illness.

In some embodiments, the systems described herein may track thetransmission of an illness. For example, if the systems described hereindetect an early indication of an illness in a user, the systemsdescribed herein may retrieve geolocation data for the user (e.g., froma location sensor such as a global positioning system sensor within awearable device) and may identify other users who came in contact withthe user recently (e.g., within the past two weeks). In some examples,the systems described herein may alert the additional users and/or oneor more team leaders of the additional users about possible infection.Additionally or alternatively, the systems described herein may attemptto determine how and/or when the user was infected by analyzing dataabout potentially previously infected users with whom the user came intocontact before displaying the early indication of the illness. In someembodiments, the systems described herein may perform contact tracing towarn potentially infected users who may have infected or been infectedby a user who is displaying early indications of an illness.

In some embodiments, the systems described herein may perform a seriesof steps to identify early indications of illness. As illustrated inFIG. 7, at step 710, one or more of the systems described herein mayidentify at least one wearable device that monitors at least onebiomarker of a user wearing the at least one wearable device during aspan of time while carrying out daily activities.

In one embodiment, the systems described herein may identify the atleast one wearable device by identifying an application to which thewearable device transmits data, and interfacing with the application toreceive the data. In one example, the wearable device may include athird-party exercise monitor. In one embodiment, the biomarker mayinclude at least one of heart rate variability, heart rate, breathingrate, temperature, and/or sleep disruption.

At step 720, one or more of the systems described herein may receive, bya server, information about activity of the at least one biomarkermonitored by the at least one wearable device during the span of time.

At step 730, one or more of the systems described herein may determinethat the activity of the at least one biomarker during the span of timeincludes an early indication of an illness.

In some embodiments, the systems described herein may determine that theactivity of the at least one biomarker during the span of time includesthe early indication the illness by correlating the activity of multiplebiomarkers received from a plurality of wearable devices. In someexamples, the systems described herein may determine that the activityof the at least one biomarker during the span of time includes the earlyindication of the illness by comparing the activity of the at least onebiomarker during the span of time to baseline activity data previouslyrecorded for the user by the at least one wearable device.

In some examples, the systems described herein may determine that theactivity of the at least one biomarker during the span of time includesthe early indication of the illness by receiving a selection of aspecific illness from a list of illnesses with known indications andanalyzing the activity of the at least one biomarker for at least oneindication of the specific illness. In some examples, the systemsdescribed herein may receive the selection of the specific illness fromthe list of illnesses by receiving a selection of the illness from ateam leader of a team that includes the user.

At step 740, one or more of the systems described herein may transmit analert about the early indication of the illness detected by the at leastone wearable device.

In some examples, the systems described herein may transmit the alert bytransmitting the alert to the user. In some examples, the systemsdescribed herein may transmit the alert by transmitting the alert to ateam leader of a team by the user.

In one embodiment, systems described herein may, in response todetermining that the activity of the at least one biomarker during thespan of time includes the early indication of the illness, (i) retrievelocation data from the location sensor of the wearable device, (ii)identify, based on the location data, at least one additional user thatcame into proximity to the user of the wearable device during the spanof time, and (iii) transmit an additional alert in response toidentifying the at least one additional user that came into proximity tothe user of the wearable device during the span of time.

The process parameters and sequence of the steps described and/orillustrated herein are given by way of example only and can be varied asdesired. For example, while the steps illustrated and/or describedherein may be shown or discussed in a particular order, these steps donot necessarily need to be performed in the order illustrated ordiscussed. The various exemplary methods described and/or illustratedherein may also omit one or more of the steps described or illustratedherein or include additional steps in addition to those disclosed.

The preceding description has been provided to enable others skilled inthe art to best utilize various aspects of the exemplary embodimentsdisclosed herein. This exemplary description is not intended to beexhaustive or to be limited to any precise form disclosed. Manymodifications and variations are possible without departing from thespirit and scope of the instant disclosure. The embodiments disclosedherein should be considered in all respects illustrative and notrestrictive. Reference should be made to the appended claims and theirequivalents in determining the scope of the instant disclosure.

Unless otherwise noted, the terms “connected to” and “coupled to” (andtheir derivatives), as used in the specification and claims, are to beconstrued as permitting both direct and indirect (i.e., via otherelements or components) connection. In addition, the terms “a” or “an,”as used in the specification and claims, are to be construed as meaning“at least one of.” Finally, for ease of use, the terms “including” and“having” (and their derivatives), as used in the specification andclaims, are interchangeable with and have the same meaning as the word“comprising.”

What is claimed is:
 1. A computer-implemented method comprising: identifying at least one wearable device that monitors at least one biomarker of a user wearing the at least one wearable device during a span of time while carrying out daily activities; receiving, by a server, information about activity of the at least one biomarker monitored by the at least one wearable device during the span of time; determining that the activity of the at least one biomarker during the span of time comprises an early indication of an illness; and transmitting an alert about the early indication of the illness detected by the at least one wearable device.
 2. The computer-implemented method of claim 1, wherein transmitting the alert comprises transmitting the alert to the user.
 3. The computer-implemented method of claim 1, wherein transmitting the alert comprises transmitting the alert to a team leader of a team comprising the user.
 4. The computer-implemented method of claim 1, wherein determining that the activity of the at least one biomarker during the span of time comprises the early indication of the illness comprises: selecting a specific illness from a list of illnesses with known indications; and analyzing the activity of the at least one biomarker for at least one indication of the specific illness.
 5. The computer-implemented method of claim 4, wherein selecting the specific illness from the list of illnesses comprises receiving a selection of the illness from a team leader of a team that comprises the user.
 6. The computer-implemented method of claim 1, wherein determining that the activity of the at least one biomarker during the span of time comprises the early indication of the illness comprises comparing the activity of the at least one biomarker during the span of time to baseline activity data previously recorded for the user by the at least one wearable device.
 7. The computer-implemented method of claim 1, identifying the at least one wearable device comprises: identifying an application to which the wearable device transmits data; and interfacing with the application to receive the data.
 8. The computer-implemented method of claim 1, wherein the biomarker comprises at least one of: heart rate; heart rate variability; breathing rate; temperature; and sleep disruption.
 9. The computer-implemented method of claim 1, wherein the wearable device comprises a third-party exercise monitor.
 10. The computer-implemented method of claim 1: wherein the wearable device comprises a location sensor; further comprising, in response to determining that the activity of the at least one biomarker during the span of time comprises the early indication of the illness: retrieving location data from the location sensor of the wearable device; identifying, based on the location data, at least one additional user that came into proximity to the user of the wearable device during the span of time; and transmitting an additional alert in response to identifying the at least one additional user that came into proximity to the user of the wearable device during the span of time.
 11. The computer-implemented method of claim 1, wherein determining that the activity of the at least one biomarker during the span of time comprises the early indication the illness comprises correlating activity of multiple biomarkers received from a plurality of wearable devices worn by the user.
 12. The computer-implemented method of claim 1, wherein the daily activities comprise at least one of: performing job functions; exercising; and sleeping.
 13. The computer-implemented method of claim 1, further comprising analyzing, by the server, a readiness of the user to perform a specified task based at least in part on the activity of the at least one biomarker during the span of time.
 14. The computer-implemented method of claim 1, wherein determining that the activity of the at least one biomarker during the span of time comprises the early indication of the illness comprises analyzing the activity of the at least one biomarker via a machine-learning classifier.
 15. The computer-implemented method of claim 14, wherein the machine-learning classifier comprises a convolutional neural network.
 16. The computer-implemented method of claim 1, wherein the wearable device comprises at least one of: a watch; a chest strap; and a ring.
 17. A system comprising: a non-transitory memory; and one or more hardware processors configured to execute instructions from the non-transitory memory to perform operations comprising: identifying at least one wearable device that monitors at least one biomarker of a user wearing the at least one wearable device during a span of time while carrying out daily activities; receiving, by a server, from the at least one wearable device, information about activity of the at least one biomarker during the span of time; determining that the activity of the at least one biomarker during the span of time comprises an early indication of an illness; and transmitting an alert about the early indication of the illness detected by the at least one wearable device.
 18. A system comprising: a monitoring application that is installed on a mobile device and that: retrieves biomarker data from at least one wearable device application installed on the mobile device; and sends the biomarker data to a server that analyzes the biomarker data for an early indication of an illness.
 19. The system of claim 18, wherein the monitoring application: receives a message from the server that the biomarker data comprises the early indication of the illness; and displays, via the mobile device, an alert about the early indication of the illness.
 20. The system of claim 18, further comprising a team leader application that is installed on a computing device and that displays at least one of: the biomarker data retrieved by the monitoring application installed on the mobile device; additional biomarker data retrieved by an instance of the monitoring application installed on an additional mobile device; and an alert about the early indication of the illness detected by the server. 